Analytical performance of the digital morphology analyzer Sysmex DI-60 for body fluid cell differential counts

Background Sysmex DI-60 (Sysmex, Kobe, Japan) is a digital morphology (DM) analyzer widely used in clinical laboratories and supports body fluid (BF) applications. We evaluated analytical performance of DI-60 compared with XN-350 (Sysmex) and manual counting for BF cell differential counts. Methods A total of 213 BF samples were collected (47 cerebrospinal fluid [CSF], 80 pleural fluid, and 86 ascites samples). The analytical performance of DI-60 for BF cell differential counts was evaluated based on sensitivity, specificity, accuracy, and agreement. BF cell differential counts obtained by DI-60 were compared with those obtained by XN-350 and manual counting. Results The overall sensitivity was high for neutrophils, lymphocytes, and macrophages (range, 83.1–99.4%). The overall specificity and overall accuracy were high for all cell types (range, 95.3–99.7% and 94.3–99.3%, respectively). The agreement between DI-60 pre-classification and verification was strong (κ = 0.89). The absolute mean differences between DI-60 verification and XN-350 ranged from 0.26 to 11.05, and differences between DI-60 verification and manual counting ranged from 0.01 to 4.76. Conclusions This is the first study to evaluate the performance of DI-60 compared with XN-350 and manual counting for BF cell differential counts. DI-60 showed reliable performance with CSF, pleural fluid, and ascites samples. For BF cell differential counts, DI-60 may be a better option than XN-350 and could be used for screening purposes in understaffed laboratories. To improve the hematology workflow for BF cell differential counting, the DM analyzer needs to be optimized by taking into account the laboratory situation and unmet needs, and the clinical laboratory needs to establish criteria for verification and manual slide review.

Introduction records and archived samples. A total of 213 BF samples (47 CSF, 80 pleural fluid, and 86 ascites samples) and their medical records were collected ( Table 1). These BF samples were obtained from subjects (median age, 66 years; interquartile range [IQR], 49-78 years) whose BF cell differential counts were requested for diagnosing diseases or monitoring health conditions from May to August 2022. The samples were collected in BD Vacutainer 1 K2 EDTA tubes (BD, Franklin Lakes, NJ, USA) and analyzed by XN-350 within 2 h after collection. Cytospin slides were prepared within 2 h after collection using the Shandon Cytospin 4 Cytocentrifuge (Thermo Fisher Scientific, Waltham, MA, USA) at 1,000 rpm for 4 min and stained using SP-50 (Sysmex) with Wright-Giemsa (RAL Diagnostics, Martillac, France).
Manual differential counting was performed according to the CLSI H56-A guidelines [4]. Two hematology experts each counted at least 100 cells on each cytocentrifuge-prepared slide at 400× magnification, and the average values were obtained for evaluation. Discrepant data between the two experts were arbitrated by a third expert. In cases where the number of cells was sufficient, 200 cells were counted if possible. In cases where the number of cells was insufficient (< 100 cells), the number of cells counted and the percentage of each cell type subsequently calculated were recorded. In manual counting, BF cells were classified into neutrophils, lymphocytes, eosinophils, basophils, and macrophages (including monocytes) according to a laboratory protocol based on the CLSI H56-A and H20-A2 guidelines [4,23].

Statistical analysis
Data are expressed as the median (IQR) or number (percentage). The performance of DI-60 pre-classification based on verification using 213 samples was evaluated based on sensitivity, specificity, positive predictive value (PPV), negative predictive value, and accuracy with their 95% confidence interval (CI). The agreement between DI-60 pre-classification and verification was evaluated using Cohen's kappa (κ) with the 95% CI, which was interpreted as follows: � 0.20, none; 0.21-0.39, minimal; 0.40-0.59, weak; 0.60-0.79, moderate; 0.80-0.90, strong; > 0.90, almost perfect [24]. In addition, the performance and agreement of DI-60 were evaluated separately for CSF, pleural fluid, and ascites samples. For the total samples, DI-60 pre-classification and verification were compared with BF cell differential counts obtained by XN-350 and/or manual counting. Wilcoxon test for paired samples, Bland-Altman plot analysis, and Passing-Bablok regression analysis were used for comparison. Pearson's correlation coefficient (r) with the 95% CI was obtained and interpreted as follows: < 0.30, negligible; 0.30-0.50, low; 0.50-0.70, moderate; 0.70-0.90, high; 0.90-1.00, very high [25]. Statistical analyses were performed using MedCalc statistical software (version 20.109; MedCalc Software, Ostend, Belgium) and Microsoft Excel software (version 2016; Microsoft Corporation, Redmond, WA, USA). Statistical results were regarded as significant if the two-sided P value was less than 0.05.

Ethics statement
This in vitro comparative study was conducted according to the Declaration of Helsinki. The Institutional Review Board (IRB) of CAUH approved the study protocol (IRB No. 2207-008-513). As this study was conducted using residual BF samples and cytospin slides after the requested test was performed, informed consent was waived according to the IRB policy. The data were analyzed anonymously.
DI-60 pre-classification showed significant differences from DI-60 verification for all cell types except neutrophils (P = 0.24). Both DI-60 pre-classification and verification showed significant differences from XN-350 for all cell types (P < 0.01, respectively) except macrophages and 'other' cells, which were not available for comparison as the cell types classified by DI-60 and XN-350 are different. Both DI-60 pre-classification and verification showed significant differences from manual counting for lymphocytes and macrophages (P < 0.01, respectively). The absolute mean differences between DI-60 pre-classification and XN-350 ranged from 0.78 to 3.48; after verification, differences between DI-60 and XN-350 ranged from 0.26 to 11.05 ( Table 5).
The absolute mean differences between DI-60 pre-classification and manual counting ranged from 0.09 to 8.39; after verification, differences between DI-60 and manual counting ranged from 0.01 to 4.76. DI-60 pre-classification and XN-350 showed a high correlation for neutrophils (r = 0.87) and lymphocytes (r = 0.88) and a low correlation for eosinophils (r = 0.43) (Fig 1). After verification, the correlation between DI-60 and XN-350 was lower for neutrophils and lymphocytes but was improved for eosinophils. DI-60 pre-classification and manual counting showed a very high correlation for neutrophils (r = 0.95), lymphocytes (r = 0.94), and macrophages (r = 0.94) and a low correlation for eosinophils (r = 0.36) (Fig 2). After verification, the correlation between DI-60 and manual counting was improved for all cell types (r = 0.78 to 0.98).

Discussion
BF samples, especially CSF, may be characterized by the instability of cellular constituents due to their chemical composition [4,7]. Therefore, BF samples should be examined within a short period of time to minimize the effect of variables related to sample storage [4,7,13]. The use of automated analyzers including automated hematology analyzers and DM analyzers may be an efficient option in terms of time and cost for the cell differential counting of BF and PB, which are known to have excellent reproducibility and accuracy [5,7,11,15]. Previous studies have compared automated hematology analyzers or DM analyzers with only manual counting using BF samples [1-3, 5-11, 14, 15]. In this study, we comprehensively evaluated the analytical performance of DI-60 for BF cell differential counts, including sensitivity, specificity, accuracy, and agreement. We also compared BF cell differential counts obtained by DI-60 with those obtained by XN-350 and manual counting. DI-60 pre-classification of BF cells based on verification showed high accuracy when using not only total samples but also CSF, pleural fluid, and ascites samples ( Table 3). DI-60 showed high sensitivity for 'other' cells in CSF samples, which was low when using pleural fluid and ascites samples. This result indicated the low number of misclassified 'other' cells in CSF samples. Although there are few cells in the 'other' cells category of CSF, DI-60 seems to be able to sensitively detect them. Regardless of the BF sample type, DI-60 demonstrated low PPVs and weak/no agreement for eosinophils and 'other' cells. A large number of the cells pre-classified as eosinophils and 'other' cells by DI-60 were misclassified ( Table 4). In a previous study evaluating DM96, another DM analyzer for BF cell differential counts, the agreement was relatively low for eosinophils and 'other' cells (55.8% and 28.9%, respectively), which is consistent with the results of our study [7]. The findings may be attributed to the low cell count of eosinophils and 'other' cells in BF and differences in the proportions of cells detected at different locations on the slide [7,[26][27][28]. A critical drawback of DM analyzers, including DI-60, is that they cannot track where the detected cells are located on the slide.
The absolute mean differences between DI-60 pre-classification and XN-350 were acceptable for all cell types (neutrophils, lymphocytes, and eosinophils); however, differences between DI-60 and XN-350 for lymphocytes were greater after verification. Differences between DI-60 and manual counting were decreased after verification for all cell types  (neutrophils, lymphocytes, eosinophils, and macrophages) and were acceptable ( Table 5). The correlation between DI-60 pre-classification and XN-350 was high for all cell types except for eosinophils; however, it was lower for neutrophils and lymphocytes after verification (Fig 1).
On the other hand, the correlation between DI-60 pre-classification and manual counting was very high for all cell types except for eosinophils, which was improved after verification for all cell types (Fig 2). The findings are similar to those of a previous study comparing BF cell differential counts between DI-60 and manual counting using 34 CSF samples and 60 other BF samples [8]. The low r value for eosinophils indicates a lower accuracy for these cells compared with other cell types. Similarly, a low correlation between the DM analyzer and manual counting has been observed for basophils in PB samples, which can be explained by a low cell count [17,18,[26][27][28]. Our findings suggest that verification of the results by DM analyzers and manual slide review are still required for BF and PB samples [12,27]. In addition, the correlation between DI-60 and XN-350, which was lower after verification, implies that BF cell differential counts obtained by DI-60 could not replace those obtained by XN-350. DI-60 and XN-350

PLOS ONE
differ in sample handling and cell type. For DI-60, a cytocentrifugation step is added during sample handling, which can improve the correlation between DI-60 and manual counting. Given that BF cell differential counting using cytospin slides is the standard method for the  This study provides baseline data on the analytical performance of DI-60 for BF cell differential counts using CSF, pleural fluid, and ascites samples. However, there are several limitations in this study. First, the cytospin slides used in this study were stained only with Wright-Giemsa from RAL Diagnostics using SP-50. The performance of DM analyzers may vary depending on the staining method and/or slide maker [29,30]. Therefore, the performance should be further compared between different slide-staining/making methods. In addition, the performance of DM analyzers is highly dependent on the quality of the slide and staining [12,31]. Clinical laboratories need to perform regular internal and external quality controls of slides and DM analyzers, even for BF samples [12,13]. Second, we compared different methods, including different analytical purposes and sample preparation procedures. DI-60 is an automated image analysis system for morphology assessment, and Romanowsky-stained slides of cytocentrifuged BF are used for analysis [4,12]. On the other hand, XN-350 is an automated cell counter for quantitative assessment and requires no special sample preparation [4,13,22]. Sample preparation may result in cell deformity or cell lysis, which may affect the analysis [8]. Therefore, it is important to consider that several factors may affect the comparison between different methods. Third, we did not evaluate the performance of DI-60 for the detection of tumor cells in BF samples. Only one pleural fluid sample and two ascites samples contained tumor cells. Although the number of samples with tumor cells was small, DI-60 identified tumor cells in all three samples and pre-classified them into the 'other' cells category. However, not all tumor cells were pre-classified into the 'other' cells category, and a considerable number of cells were pre-classified into artifacts category. This outcome may be explained by the formation of clusters from tumor cells, and the detection of such clusters is still difficult [4,9].
In conclusion, this is the first study to evaluate the performance of DI-60 compared with XN-350 and manual counting for BF cell differential counts. DI-60 showed reliable analytical performance with improvement after verification when using CSF, pleural fluid, and ascites samples. For BF cell differential counts, DI-60 may be a better option than XN-350 and could be used for screening purposes in understaffed laboratories. However, DI-60 cannot completely replace the gold standard method, i.e., manual counting. Verification and manual slide review are still required, especially for samples with large numbers of cells pre-classified into the eosinophils and 'other' cells categories. To improve the hematology workflow for BF cell differential counting, the DM analyzer needs to be optimized by taking into account the laboratory situation and unmet needs, and the clinical laboratory needs to establish criteria for verification and manual slide review.